Motion segmentation using curve fitting on Lagrangian particle trajectories

In this paper we present a segmentation algorithm to extract foreground object motion in a moving camera scenario without any preprocessing step such as tracking selected features, video alignment, or foreground segmentation. By viewing it as a curve fitting problem on advected particle trajectories, we use RANSAC to find the polynomial that best fits the camera motion and identify all trajectories that correspond to the camera motion. The remaining trajectories are those due to the foreground motion. By using the superposition principle, we subtract the motion due to camera from foreground trajectories and obtain the true object-induced trajectories. We show that our method performs on par with state-of-the-art technique, with an execution time speed-up of 10x-40x. We compare the results on real-world datasets such as UCF-ARG, UCF Sports and Liris-HARL. We further show that it can be used toper-form video alignment.

Superparamagnetic behaviour and T-1, T-2 relaxivity of ZnFe2O4 nanoparticles for magnetic resonance imaging

In the present study, ZnFe2O4 nanoparticles were synthesized by the chemical co-precipitation followed by calcinations at 473 and 673K for 4h. Particle sizes obtained were 4 and 6nm for the calcination temperatures of 473 and 673K, respectively. To study the origin of system's low temperature spin dynamic behaviour, temperature dependence of susceptibility was investigated as a function of particle size and frequency. Slight increase in the grain size from 4nm at 473K to 6nm at 673K has led to a peak shift of temperature dependence of susceptibility measured at a constant frequency of 400Hz. Temperature dependence of at different frequencies also resulted in peak shift. Relaxation time dependence of peak temperature obeys a power law, which provides the fitting parameters within the range of superparamagnetic nature of the particles. Further, dependence of relaxation time and peak temperature obeys VogelFulcher law rather than NeelBrown equation demonstrating that the particles follow the behaviour of superparamagnetism of slightly interacting system. Spinlattice, T-1 and spinspin, T-2 relaxivity of proton of the water molecule in the presence of chitosan-coated superparamagnetic ZnFe2O4 nanoparticle yields the values of 0.002 and 0.360s(1)perppm.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Automatic speech segmentation using probabilistic latent component modeling

Latent variable methods, such as PLCA (Probabilistic Latent Component Analysis) have been successfully used for analysis of non-negative signal representations. In this paper, we formulate PLCS (Probabilistic Latent Component Segmentation), which models each time frame of a spectrogram as a spectral distribution. Given the signal spectrogram, the segmentation boundaries are estimated using a maximum-likelihood approach. For an efficient solution, the algorithm imposes a hard constraint that each segment is modelled by a single latent component. The hard constraint facilitates the solution of ML boundary estimation using dynamic programming. The PLCS framework does not impose a parametric assumption unlike earlier ML segmentation techniques. PLCS can be naturally extended to model coarticulation between successive phones. Experiments on the TIMIT corpus show that the proposed technique is promising compared to most state of the art speech segmentation algorithms.

Imaging Intracellular Zinc by Using a Glyoxal Bis(4-methyl-4-phenyl-3-thiosemicarbazone) Ligand

The ligand glyoxal bis(4-methyl-4-phenyl-3-thiosemicarbazone) (GTSCH2) is shown to be a selective fluorescence turn-on sensor for zinc ions (Zn2+). This sensor is easy to synthesize, exhibits excellent sensitivity and selectivity towards Zn2+ over other physiologically relevant cations, and has sub-nanomolar binding affinity. It displays maximum fluorescence response to Zn2+ when the metal/ligand ratio is 1:1 and displays stable fluorescence over a broad pH range. The potential of GTSCH2 to image Zn2+ inside the cell was demonstrated in MCF-7 cells (human breast cancer cell line) by using flow cytometry and confocal fluorescence microscopy. Cell viability studies reveal that the probe is biocompatible and suitable for cellular applications.

MRT Letter: Light Sheet Based Imaging Flow Cytometry on a Microfluidic Platform

We propose a light sheet based imaging flow cytometry technique for simultaneous counting and imaging of cells on a microfluidic platform. Light sheet covers the entire microfluidic channel and thus omits the necessity of flow focusing and point scanning based technology. Another advantage lies in the orthogonal detection geometry that totally cuts-off the incident light, thereby substantially reducing the background in the detection. Compared to the existing state-of-art techniques the proposed technique shows marked improvement. Using fluorescently-coated Saccharomyces cerevisiae cells we have recorded cell counting with throughput as high as 2,090 cells/min in the low flow rate regime and were able to image the individual cells on-the-go. Overall, the proposed system is cost-effective and simple in channel geometry with the advantage of efficient counting in operational regime of low laminar flow. This technique may advance the emerging field of microfluidic based cytometry for applications in nanomedicine and point of care diagnostics. Microsc. Res. Tech. 76:1101-1107, 2013. (c) 2013 Wiley Periodicals, Inc.

Image reconstruction enables high resolution imaging at large penetration depths in fluorescence microscopy

Imaging thick specimen at a large penetration depth is a challenge in biophysics and material science. Refractive index mismatch results in spherical aberration that is responsible for streaking artifacts, while Poissonian nature of photon emission and scattering introduces noise in the acquired three-dimensional image. To overcome these unwanted artifacts, we introduced a two-fold approach: first, point-spread function modeling with correction for spherical aberration and second, employing maximum-likelihood reconstruction technique to eliminate noise. Experimental results on fluorescent nano-beads and fluorescently coated yeast cells (encaged in Agarose gel) shows substantial minimization of artifacts. The noise is substantially suppressed, whereas the side-lobes (generated by streaking effect) drops by 48.6% as compared to raw data at a depth of 150 mu m. Proposed imaging technique can be integrated to sophisticated fluorescence imaging techniques for rendering high resolution beyond 150 mu m mark. (C) 2013 AIP Publishing LLC.

Monte Carlo simulation of light transport in tissue for optimizing light delivery in photoacoustic imaging of the sentinel lymph node

Noninvasive or minimally invasive identification of sentinel lymph node (SLN) is essential to reduce the surgical effects of SLN biopsy. Photoacoustic (PA) imaging of SLN in animal models has shown its promise for clinical use in the future. Here, we present a Monte Carlo simulation for light transport in the SLN for various light delivery configurations with a clinical ultrasound probe. Our simulation assumes a realistic tissue layer model and also can handle the transmission/reflectance at SLN-tissue boundary due to the mismatch of refractive index. Various light incidence angles show that for deeply situated SLNs the maximum absorption of light in the SLN is for normal incidence. We also show that if a part of the diffused reflected photons is reflected back into the skin using a reflector, the absorption of light in the SLN can be increased significantly to enhance the PA signal. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)

NESP: Nonlinear enhancement and selection of plane for optimal segmentation and recognition of scene word images

In this paper, we report a breakthrough result on the difficult task of segmentation and recognition of coloured text from the word image dataset of ICDAR robust reading competition challenge 2: reading text in scene images. We split the word image into individual colour, gray and lightness planes and enhance the contrast of each of these planes independently by a power-law transform. The discrimination factor of each plane is computed as the maximum between-class variance used in Otsu thresholding. The plane that has maximum discrimination factor is selected for segmentation. The trial version of Omnipage OCR is then used on the binarized words for recognition. Our recognition results on ICDAR 2011 and ICDAR 2003 word datasets are compared with those reported in the literature. As baseline, the images binarized by simple global and local thresholding techniques were also recognized. The word recognition rate obtained by our non-linear enhancement and selection of plance method is 72.8% and 66.2% for ICDAR 2011 and 2003 word datasets, respectively. We have created ground-truth for each image at the pixel level to benchmark these datasets using a toolkit developed by us. The recognition rate of benchmarked images is 86.7% and 83.9% for ICDAR 2011 and 2003 datasets, respectively.

Evaluation of document binarization using eigen value decomposition

A necessary step for the recognition of scanned documents is binarization, which is essentially the segmentation of the document. In order to binarize a scanned document, we can find several algorithms in the literature. What is the best binarization result for a given document image? To answer this question, a user needs to check different binarization algorithms for suitability, since different algorithms may work better for different type of documents. Manually choosing the best from a set of binarized documents is time consuming. To automate the selection of the best segmented document, either we need to use ground-truth of the document or propose an evaluation metric. If ground-truth is available, then precision and recall can be used to choose the best binarized document. What is the case, when ground-truth is not available? Can we come up with a metric which evaluates these binarized documents? Hence, we propose a metric to evaluate binarized document images using eigen value decomposition. We have evaluated this measure on DIBCO and H-DIBCO datasets. The proposed method chooses the best binarized document that is close to the ground-truth of the document.

Studies on thin film based flexible gold electrode arrays for resistivity imaging in electrical impedance tomography

Practical phantoms are essential to assess the electrical impedance tomography (EIT) systems for their validation, calibration and comparison purposes. Metal surface electrodes are generally used in practical phantoms which reduce the SNR of the boundary data due to their design and development errors. Novel flexible and biocompatible gold electrode arrays of high geometric precision are proposed to improve the boundary data quality in EIT. The flexible gold electrode arrays are developed on flexible FR4 sheets using thin film technology and practical gold electrode phantoms are developed with different configurations. Injecting a constant current to the phantom boundary the surface potentials are measured by a LabVIEW based data acquisition system and the resistivity images are reconstructed in EIDORS. Boundary data profile and the resistivity images obtained from the gold electrode phantoms are compared with identical phantoms developed with stainless steel electrodes. Surface profilometry, microscopy and the impedance spectroscopy show that the gold electrode arrays are smooth, geometrically precised and less resistive. Results show that the boundary data accuracy and image quality are improved with gold electrode arrays. Results show that the diametric resistivity plot (DRP), contrast to noise ratio (CNR), percentage of contrast recovery (PCR) and coefficient of contrast (COC) of reconstructed images are improved in gold electrode phantoms. (C) 2013 Elsevier Ltd. All rights reserved.

Carbohydrate-appended photocytotoxic (imidazophenanthroline)-oxovanadium(IV) complexes for cellular targeting and imaging

Oxovanadium(IV) complexes VO(aip)(L)](ClO4)(2) (L = phtpy, 1; stpy, 2) and VO(pyip)(L)](ClO4)(2) (L = phtpy, 3; stpy, 4), where aip is 2-(9-anthryl)-1H-imidazo4,5-f]1,10] phenanthroline, pyip is 2-(1-pyrenyl)-1Himidazo4,5-f]1,10] phenanthroline, phtpy is (4'-phenyl)-2,2': 6',2 `'-terpyridine and stpy is (2,2': 6', 2 `'-terpyridin-4'-oxy) ethyl-beta-D-glucopyranoside, were prepared, characterized and their DNA binding and photocleavage activity, cellular uptake and photocytotoxicity in visible light were studied. The complexes are avid binders to calf thymus DNA (K-b similar to 10(5) mol(-1)). They efficiently cleave pUC19 DNA in red light of 705 nm via the formation of HO center dot species. The glucose appended complexes 2 and 4 showed higher photocytotoxicity in HeLa and Hep G2 cells over the normal HEK 293T cells. No such preference was observed for the phtpy complexes 1 and 3. No significant difference in IC50 values was observed for the HEK 293T cells. Cell cycle analysis showed that the glucose appended complexes 2 and 4 are more photocytotoxic in cancer cells than in normal cells. Fluorescence microscopy was done to study the cellular localization of complex 4 having a pendant pyrenyl group.

Imaging cellular signalling: many `moving tales' in MAP kinase odyssey

Cellular signalling events are at the core of every adaptive response. Signalling events link environmental changes to physiological responses, consequently allowing cellular and organismal sustenance and survival. Classical approaches to study cellular signalling have relied on a variety of cell disruptive techniques which yield limited kinetic information, while the underlying events are much more complex. In this article, we discuss how modern live cell imaging microscopy has found increasing utilization in revealing spatio temporal dynamics of various signalling pathways. Utilizing the well studied mitogen-activated protein kinase (MAPK) signalling cascade as a template, the design, construction and utilization of `mobile' (translocation proficient) biosensors, suitable for studying MAPK signalling in living cells are described in detail. Experimental setup and results obtained from these biosensors, based on different proteins involved in the MAPK signalling cascade, have been described along with the setup of a microscope optimal for live cell imaging applications. Utilizing the ability to activate or deactivate signalling pathways using defined activators and specific pharmacological inhibitors, we also show how these sensors can yield unique spatial and temporal kinetic information of signalling in living cells.

Approximate Nearest Neighbour Field based Optic Disk Detection

Approximate Nearest Neighbour Field maps are commonly used by computer vision and graphics community to deal with problems like image completion, retargetting, denoising, etc. In this paper, we extend the scope of usage of ANNF maps to medical image analysis, more specifically to optic disk detection in retinal images. In the analysis of retinal images, optic disk detection plays an important role since it simplifies the segmentation of optic disk and other retinal structures. The proposed approach uses FeatureMatch, an ANNF algorithm, to find the correspondence between a chosen optic disk reference image and any given query image. This correspondence provides a distribution of patches in the query image that are closest to patches in the reference image. The likelihood map obtained from the distribution of patches in query image is used for optic disk detection. The proposed approach is evaluated on five publicly available DIARETDB0, DIARETDB1, DRIVE, STARE and MESSIDOR databases, with total of 1540 images. We show, experimentally, that our proposed approach achieves an average detection accuracy of 99% and an average computation time of 0.2 s per image. (C) 2013 Elsevier Ltd. All rights reserved.

Co-registration of speech production datasets from electromagnetic articulography and real-time magnetic resonance imaging

This paper describes a spatio-temporal registration approach for speech articulation data obtained from electromagnetic articulography (EMA) and real-time Magnetic Resonance Imaging (rtMRI). This is motivated by the potential for combining the complementary advantages of both types of data. The registration method is validated on EMA and rtMRI datasets obtained at different times, but using the same stimuli. The aligned corpus offers the advantages of high temporal resolution (from EMA) and a complete mid-sagittal view (from rtMRI). The co-registration also yields optimum placement of EMA sensors as articulatory landmarks on the magnetic resonance images, thus providing richer spatio-temporal information about articulatory dynamics. (C) 2014 Acoustical Society of America